# The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale

arXiv: https://arxiv.org/abs/2610.18511 · HTML: https://mlsanity.com/paper/2610.18511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 19 of 19)

- [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023)
- [Foundation Models for Decision Making: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2303.04129/) (2023)
- [A Review of Deep Reinforcement Learning for Smart Building Energy Management](https://mlsanity.com/paper/2008.05074/) (2020)
- [Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics](https://mlsanity.com/paper/2011.05987/) (2020)
- [Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings](https://mlsanity.com/paper/2006.14156/) (2020)
- [A Review of Reinforcement Learning for Autonomous Building Energy Management](https://mlsanity.com/paper/1903.05196/) (2019)
- [Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms](https://mlsanity.com/paper/2510.23166/) (2025)
- [GenTL: A General Transfer Learning Model for Building Thermal Dynamics](https://mlsanity.com/paper/2501.13703/) (2025)
- [AI Must Embrace Specialization via Superhuman Adaptable Intelligence](https://mlsanity.com/paper/2602.23643/) (2026)
- [The 10th AI City Challenge](https://mlsanity.com/paper/2608.17044/) (2026)
- [Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts](https://mlsanity.com/paper/2508.21615/) (2025)
- [A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings](https://mlsanity.com/paper/2310.08569/) (2023)
- [The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability](https://mlsanity.com/paper/2410.03756/) (2024)
- [A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control](https://mlsanity.com/paper/2308.05711/) (2023)
- [Physics-informed machine learning for building performance simulation-A review of a nascent field](https://mlsanity.com/paper/2504.00937/) (2025)
- [BUILDA: A Thermal Building Data Generation Framework for Transfer Learning](https://mlsanity.com/paper/2508.12703/) (2025)
- [BESTOpt: A Modular, Physics-Informed Machine Learning based Building Modeling, Control and Optimization Framework](https://mlsanity.com/paper/2601.16283/) (2026)
- [Tensorized Radiative Heat Transfer for a Scalable and Calibrated Building Energy Simulator](https://mlsanity.com/paper/2605.29003/) (2026)
- [Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning](https://mlsanity.com/paper/2607.16534/) (2026)

## Abstract (arXiv)

Buildings cause nearly 40% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60% and account for 15% of global energy consumption. Despite advances in artificial intelligence, most buildings still operate as inefficiently as they did decades ago. Building optimization, perhaps more so than any other optimization problem, has enormous potential for Machine Learning (ML) driven impact on climate change, in both the short and long term. One of the biggest obstacles for building optimization is the lack of policy transfer. Most building control systems, whether simulation or learning based, don't generalize: they only work well for the building they were developed for, but not for other buildings. To solve this problem, we propose a challenge for the development of building foundation models that easily adapt to new buildings with minimal configuration and thereby enable building optimization to scale across the globe. Participants will build control policies on a set of simulated buildings, and have their policies evaluated on an unseen set of buildings. The buildings vary in size, layout, weather, material properties, and HVAC system. Successful outcomes from this initiative promise a massive leap forward in global sustainability efforts and demonstrate the great potential of ML to combat climate change.